Title

Authors

Date of this Version

Summer 6-19-2015

Comments

A DISSERTATION Presented to the Faculty of The Graduate College at the University of Nebraska In Partial Fulfillment of Requirements For the Degree of Doctor of Philosophy, Major: Engineering (Construction Engineering and Management), Under the Supervision of Professors Terence Foster and Eddy Rojas. Lincoln, Nebraska: August, 2015

Copyright (c) 2015 Krishna Prasad Kisi

Abstract

In an attempt to evaluate the efficiency of labor-intensive construction operations, project managers typically compare actual with historical productivity for equivalent operations. However, this approach toward examining productivity only provides a relative benchmark for efficiency and may lead to the characterization of operations as objectively efficient when in reality such operations might simply be comparably efficient.

Optimal productivity is the highest sustainable productivity achievable under good management and typical field conditions. Optimal productivity is useful in the determination of the absolute efficiency of construction operations because an accurate estimate of optimal labor productivity allows for the comparison of actual vs. optimal (unbiased) rather than actual vs. historical (biased) productivity.

This research contributes to the body of knowledge by introducing a two-prong strategy for estimating optimal productivity in labor-intensive construction operations and applying it in an activity with a single worker and sequential tasks as well as in an activity with multiple workers and sequential and parallel tasks. The first prong, or a top-down approach, estimates the upper limit of optimal productivity by introducing system inefficiencies into the productivity frontier – productivity achieved under perfect conditions. A qualitative factor model is used to achieve this objective. The second prong, or a bottom-up approach, estimates the lower limit of optimal productivity by taking away operational inefficiency from actual productivity – productivity recorded in the field. A discrete event simulation model is used to estimate this value. An average of the upper and lower limits is taken as the best estimate of optimal productivity.